docs: add research-backed prompt design and developer guides

- Add research-backed-prompt-design.md with Stanford/Anthropic patterns
- Add openagent.md documentation with workflow and delegation details
- Add developer guides for slash commands and context system
- Document position sensitivity, nesting limits, and instruction ratios
- Include examples and best practices for prompt optimization
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darrenhinde 2025-11-19 01:23:27 +00:00
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# OpenCode File Structure & Reference Guide
## Directory Structure
### Global Config (`~/.config/opencode/`)
```
~/.config/opencode/
├── opencode.json # Global config
├── AGENTS.md # Auto-loaded instructions
├── CLAUDE.md # Auto-loaded instructions
├── agent/ # Custom agents
│ ├── my-agent.md
│ └── category/
│ └── nested-agent.md
├── command/ # Custom commands
│ └── my-command.md
└── plugin/ # Custom plugins
└── my-plugin.ts
```
### Local Repo (`.opencode/`)
```
your-repo/
├── opencode.json # Repo config
├── .opencode/
│ ├── AGENTS.md # Auto-loaded instructions
│ ├── agent/ # Project-specific agents
│ │ └── my-agent.md
│ ├── command/ # Project-specific commands
│ │ └── my-command.md
│ └── plugin/ # Project-specific plugins
│ └── my-plugin.ts
└── src/
```
## Auto-Loaded Files
**Instruction files** (loaded automatically as system prompts):
- `AGENTS.md` - Custom instructions (global or local)
- `CLAUDE.md` - Legacy Claude instructions
- `CONTEXT.md` - Deprecated, but still works
**Config files** (merged in order):
1. `~/.config/opencode/opencode.json` (global)
2. `opencode.json` files from repo root up to current directory
3. Files from `.opencode/` folders in hierarchy
## Supported Subfolders
| Folder | Files | Purpose |
|--------|-------|---------|
| `agent/` | `*.md` | Custom agents with system prompts |
| `command/` | `*.md` | Custom slash commands |
| `plugin/` | `*.ts`, `*.js` | Custom tools and extensions |
**⚠️ Use singular names only:** `agent/`, NOT `agents/`
## File References with `@` Symbol
**In commands and templates:**
```bash
# Relative to repo root
@README.md
@src/main.ts
# Home directory
@~/my-file.txt
# Absolute path
@/absolute/path/file.txt
# If file doesn't exist, looks for agent
@my-agent
```
**Resolution order:**
1. Check if starts with `~/` → resolve to home directory
2. Check if absolute path → use as-is
3. Otherwise → resolve relative to repo root (`Instance.worktree`)
4. If not found → look for agent with that name
## Custom Instruction Files
**For arbitrary paths, use `instructions` field:**
```json
{
"instructions": [
"~/opencode/context/my-context.md",
"docs/**/*.md",
".opencode/context/**/*.md"
]
}
```
**Paths can be:**
- Absolute: `/path/to/file.md`
- Home relative: `~/path/to/file.md`
- Repo relative: `docs/instructions.md`
- Glob patterns: `**/*.md`
## Config Merging
**Configs merge with priority** (later overrides earlier):
1. Global config (`~/.config/opencode/`)
2. Repo root configs (from root up)
3. Custom config directories (`.opencode/` folders)
4. Environment variables (`OPENCODE_CONFIG`)
**Agents, commands, and plugins** from all locations are merged together.
## Quick Reference
| What | Where | How |
|------|-------|-----|
| Global agent | `~/.config/opencode/agent/name.md` | Auto-loaded |
| Local agent | `.opencode/agent/name.md` | Auto-loaded |
| Global command | `~/.config/opencode/command/name.md` | Auto-loaded |
| Local command | `.opencode/command/name.md` | Auto-loaded |
| Global instructions | `~/.config/opencode/AGENTS.md` | Auto-loaded |
| Local instructions | `.opencode/AGENTS.md` or `AGENTS.md` | Auto-loaded |
| Custom files | Anywhere | Use `instructions` config or `@` symbol |

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# Clear Guidelines: Main Agent → Slash Command → Subagent
## The Complete Pattern
### Step 1: Create Your Command (Template Only)
```markdown
# .opencode/command/security/scan.md
---
description: "Security vulnerability scan"
# NO agent field - let caller decide
---
Perform security scan on: $ARGUMENTS
@$1
@AGENTS.md
@.cursorrules
Check for:
- SQL injection
- XSS vulnerabilities
- Authentication issues
```
### Step 2: Create Your Subagent
```markdown
# .opencode/agent/security/scanner.md
---
mode: "subagent"
description: "Security vulnerability detection specialist"
tools:
read: true
grep: true
---
You are a security expert. Scan code for vulnerabilities.
```
### Step 3: Document in AGENTS.md (CRITICAL)
```markdown
# AGENTS.md
## Slash Commands for Subagents
When you need specialized workflows, use the Task tool with slash commands:
### Pattern
```
Task(
subagent_type="[subagent-name]",
prompt="/command-name arguments"
)
```
### Available Commands
**Security:**
- `/security/scan [file]`
- Use: `Task(subagent_type="security/scanner", prompt="/security/scan auth.ts")`
- Performs comprehensive security analysis
**Testing:**
- `/test/generate [file]`
- Use: `Task(subagent_type="testing/generator", prompt="/test/generate service.ts")`
- Generates unit tests with project patterns
**Refactoring:**
- `/refactor/modernize [file] [context]`
- Use: `Task(subagent_type="refactor/modernizer", prompt="/refactor/modernize old.js 'use async/await'")`
- Updates legacy code to modern standards
### When to Use
- User asks for security audit → Use `/security/scan`
- After writing code → Use `/test/generate`
- Legacy code mentioned → Use `/refactor/modernize`
```
## How It Works
```
User: "Check auth.ts for security issues"
↓
Main Agent reads AGENTS.md
↓
Sees: /security/scan pattern
↓
Main Agent calls:
Task(
subagent_type="security/scanner",
prompt="/security/scan auth.ts"
)
↓
System processes /security/scan command:
- Loads template from command/security/scan.md
- Replaces $ARGUMENTS with "auth.ts"
- Attaches @auth.ts file
- Attaches @AGENTS.md, @.cursorrules
↓
Subagent "security/scanner" receives:
- Full template text
- All attached files as context
↓
Subagent executes → Returns result → Main agent responds to user
```
## Complete Working Example
### Files Structure
```
.opencode/
├── agent/
│ ├── security/
│ │ └── scanner.md
│ ├── testing/
│ │ └── generator.md
│ └── refactor/
│ └── modernizer.md
├── command/
│ ├── security/
│ │ └── scan.md
│ ├── test/
│ │ └── generate.md
│ └── refactor/
│ └── modernize.md
└── AGENTS.md ← Documents the patterns
```
### 1. Command Template
```markdown
# .opencode/command/test/generate.md
---
description: "Generate comprehensive unit tests"
---
Generate unit tests for: $ARGUMENTS
Target file:
@$1
Existing test patterns:
!`find $(dirname $1) -name "*.test.*" | head -3`
Requirements:
- Test all public methods
- Include edge cases
- Mock external dependencies
- Follow project conventions from @AGENTS.md
```
### 2. Subagent
```markdown
# .opencode/agent/testing/generator.md
---
mode: "subagent"
description: "Use AFTER writing new code to generate comprehensive tests"
tools:
read: true
write: true
grep: true
---
You generate unit tests by:
1. Analyzing the implementation
2. Studying existing test patterns
3. Creating thorough test coverage
4. Following project conventions
```
### 3. AGENTS.md Documentation
```markdown
# AGENTS.md
## Build/Test Commands
- Run tests: `npm test`
- Single test: `npm test -- path/to/test.ts`
## Code Standards
- TypeScript strict mode
- camelCase for functions
- PascalCase for classes
## Slash Commands with Subagents
Use Task tool to invoke specialized workflows:
### Testing Workflow
After writing new code, generate tests:
```
Task(
subagent_type="testing/generator",
prompt="/test/generate src/services/payment.ts"
)
```
### Security Workflow
Before deploying authentication code:
```
Task(
subagent_type="security/scanner",
prompt="/security/scan src/auth/*.ts"
)
```
### Refactoring Workflow
When updating legacy code:
```
Task(
subagent_type="refactor/modernizer",
prompt="/refactor/modernize src/legacy/util.js 'convert to TypeScript with async/await'"
)
```
### Pattern Summary
1. Identify the workflow need
2. Use Task tool with appropriate subagent_type
3. Pass slash command as the prompt
4. Command loads template + context
5. Subagent executes with full context
```
## Real Conversation Example
```
User: "I just wrote a new payment service. Can you add tests?"
Main Agent (reads AGENTS.md, sees testing workflow):
"I'll generate comprehensive tests for the payment service."
[Calls Task tool:]
Task(
description="Generate payment service tests",
subagent_type="testing/generator",
prompt="/test/generate src/services/payment.ts"
)
Command System:
- Loads .opencode/command/test/generate.md
- Replaces $1 with "src/services/payment.ts"
- Attaches payment.ts file
- Finds existing test files in same directory
- Attaches AGENTS.md for conventions
Subagent (testing/generator):
- Receives full context
- Analyzes payment.ts implementation
- Studies existing test patterns
- Generates comprehensive tests
- Returns result
Main Agent:
"✅ Generated comprehensive tests in src/services/payment.test.ts
- Tests all public methods
- Includes edge cases for failed payments
- Mocks payment gateway
- Follows project test conventions"
```
## Template for Your AGENTS.md
```markdown
# AGENTS.md
## Project Commands
[Your build/test commands]
## Code Standards
[Your standards]
## Automated Workflows
I have specialized workflows accessible via slash commands.
Use the Task tool to invoke them with appropriate subagents.
### Pattern
```
Task(
subagent_type="[subagent-name]",
prompt="/[command-name] [arguments]"
)
```
### Security Analysis
**When:** Before deploying auth/payment code
**Command:** `/security/scan [file]`
**Subagent:** `security/scanner`
**Example:**
```
Task(
subagent_type="security/scanner",
prompt="/security/scan src/auth/jwt.ts"
)
```
### Test Generation
**When:** After writing new code
**Command:** `/test/generate [file]`
**Subagent:** `testing/generator`
**Example:**
```
Task(
subagent_type="testing/generator",
prompt="/test/generate src/services/payment.ts"
)
```
### Code Modernization
**When:** Refactoring legacy code
**Command:** `/refactor/modernize [file] [instructions]`
**Subagent:** `refactor/modernizer`
**Example:**
```
Task(
subagent_type="refactor/modernizer",
prompt="/refactor/modernize src/old-api.js 'convert to TypeScript with async/await'"
)
```
### Quick Reference
| Task | Command | Subagent |
|------|---------|----------|
| Security scan | `/security/scan` | `security/scanner` |
| Generate tests | `/test/generate` | `testing/generator` |
| Modernize code | `/refactor/modernize` | `refactor/modernizer` |
## Notes
- Commands are templates that attach context files automatically
- Subagents receive full context and execute specialized workflows
- Always specify subagent_type when using Task tool with slash commands
```
## Key Points
1. **Commands are templates** - They don't execute anything, they just format context
2. **Subagents do the work** - They receive the processed template + attached files
3. **AGENTS.md is the bridge** - It tells main agent HOW to connect commands to subagents
4. **Pattern is always:** `Task(subagent_type="X", prompt="/command args")`
5. **Main agent decides** which subagent to use based on your documentation
## Checklist
- ✅ Create command templates (no agent field)
- ✅ Create specialized subagents (mode: "subagent")
- ✅ Document pattern in AGENTS.md with examples
- ✅ Include "When to use" triggers
- ✅ Show exact Task tool syntax
- ✅ Test with: "Can you [task description]"
That's it! Main agent reads AGENTS.md, sees the patterns, and knows how to invoke slash commands through subagents.

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@ -1139,4 +1139,26 @@ OpenAgent is configured in `.opencode/agent/openagent.md`. You can customize:
- Test failure protocol
- And more!
### Recent Optimizations (Nov 2025)
OpenAgent has been optimized based on research-backed prompt engineering patterns:
✅ **Critical rules positioned early** - Safety rules now appear in the first 15% of the prompt (10.7% position)
✅ **Minimal nesting complexity** - Flattened XML structure to only 2 levels for maximum clarity
✅ **Modular design** - Session management and context discovery extracted to reference files
✅ **Explicit prioritization** - 3-tier priority system for conflict resolution
✅ **Single source of truth** - Critical rules defined once and referenced throughout
**Effectiveness improvements are model- and task-specific.** These patterns are validated by Stanford/Anthropic research but actual performance gains vary based on your specific use case.
### Required Context Files
OpenAgent requires these context files to function properly:
- `.opencode/context/core/session-management.md` - Session lifecycle and cleanup policies
- `.opencode/context/core/context-discovery.md` - Dynamic context loading and discovery
- `.opencode/context/core/context-management.md` - Overall context management strategy
- `.opencode/context/core/essential-patterns.md` - Core coding patterns and best practices
These files are automatically loaded when OpenAgent starts.
Happy building! 🚀

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# Updated Agent Prompt Design Flow: Empirically Validated Framework
**Key Takeaway:**
The overall flow—context-driven, safety-aware, multi-stage orchestration using XML structure—is architecturally robust and aligns well with published best practices. However, the effectiveness of specific ordering, placement, and ratios is task-dependent. No universal percentages or absolute gains can be cited from research, but these design choices are supported by empirical studies, which inform _how_ and _why_ each component helps improve agent safety, reliability, and output clarity.
---
## 1. Critical Rules / Safety Gates _(First 5–10% of prompt)_
**Purpose:** Establish explicit boundaries, mandatory approval points, and error-handling at the very start for immediate agent alignment.
**XML Block:**
```xml
<critical_rules priority="highest">
<rule>ALWAYS request approval before ANY execution</rule>
<rule>NEVER auto-fix issues without explicit user approval</rule>
<rule>STOP immediately on test failure</rule>
<rule>Confirm before cleaning up files</rule>
</critical_rules>
```
**Why This Works:**
- **Southampton NAACL 2024**: Early placement of critical instructions improves adherence, but magnitude depends on the task. No single "best" position; position sensitivity is real.[1]
- **Anthropic & Industry Docs:** Early, flat rules elevate attention and safety for LLMs.[2]
---
## 2. Context Hierarchy _(Next 15–25%)_
**Purpose:** Provide a layered, hierarchical context—from the system level down to current execution—before specifying tasks or instructions.
**XML Block:**
```xml
<context_hierarchy>
<system_context>Universal AI agent orchestration</system_context>
<domain_context>Multi-agent workflow coordination</domain_context>
<task_context>User request analysis and routing</task_context>
<execution_context>Current user session, tools available</execution_context>
</context_hierarchy>
```
**Why This Works:**
- **Stanford/NAACL Studies**: Context aids multi-step task adherence, though not universally; broad-to-narrow ordering improves clarity for complex workflows.[3]
- **AWS Agent Patterns**: Hierarchical context reduces cognitive load and token wastage.[4]
---
## 3. Role Definition _(First 20–30%)_
**Purpose:** State the agent's persona, scope, and operational constraints up front.
**XML Block:**
```xml
<role>
<identity>OpenAgent: Universal Coordinator</identity>
<capabilities>Answering, executing, workflow management</capabilities>
<scope>Any domain, adaptive</scope>
<constraints>Safety, approval, human-centric</constraints>
</role>
```
**Why This Works:**
- **Anthropic Docs:** Early role tagging increases output coherence and agent "persona" adherence; quantified benefit is task/model-specific.[2]
- **Role Prompting Research:** Early persona setting improves adherence in multi-instruction chains.[5]
---
## 4. Execution Path Decision _(25–35%)_
**Purpose:** Quickly branch into conversational or task workflow execution based on the query type.
**XML Block:**
```xml
<execution_paths>
<decision>
<if trigger="simple_question">route="conversational"</if>
<if trigger="task_or_execution">route="task_workflow"</if>
</decision>
</execution_paths>
```
**Why This Works:**
- **Microsoft/AWS/Recent Studies:** Adaptive branching reduces overhead and improves efficiency.[6]
---
## 5A. Conversational Path _(If simple informational query)_
**XML Block:**
```xml
<conversational_path>
<trigger>simple_question</trigger>
<execution>
<step>Analyze request and context</step>
<step>Answer clearly and directly</step>
<style>Lean, conversational</style>
</execution>
</conversational_path>
```
**Why This Works:**
- **Lean Design Principle:** Simplified pathways reduce cognitive and token overhead for basic tasks.[7]
---
## 5B. Task Workflow _(If executing a complex or delegated task)_
**XML Block:**
```xml
<task_workflow>
<stage name="Analyze">Assess request complexity & dependencies</stage>
<stage name="Plan">Draft stepwise execution plan</stage>
<stage name="Approval">Present plan and request user approval</stage>
<stage name="Execute">Carry out approved steps</stage>
<stage name="Validate">Test outputs, report and propose fixes if needed</stage>
<stage name="Summarize">Formally summarize results, next steps</stage>
<stage name="Complete">Confirm user satisfaction and session cleanup</stage>
</task_workflow>
```
**Why This Works:**
- **AWS Workflow/Multi-Agent Orchestration:** Staged, approval-gated flows improve output accuracy and user trust, with benefits contingent on workflow complexity.[8][4]
---
## 6. Delegation Logic _(50–65%)_
**Purpose:** Clearly define when to call subagents vs. direct execution.
**XML Block:**
```xml
<delegation_criteria>
<route agent="@subagent/core/task-manager" category="features">
<when>Feature spans multiple files | effort > 60 min | complex dependencies</when>
<context_inheritance>Load session context from manifest</context_inheritance>
</route>
<direct_execution>
<when>Single file; simple edit; direct user request</when>
</direct_execution>
</delegation_criteria>
```
**Why This Works:**
- **Recent Multi-Agent Studies:** Explicit delegation criteria and context inheritance improves reliability and output quality.[9][8]
---
## 7. Session & Context Management _(65–75%)_
**Purpose:** Lazy session creation for resource efficiency, manifest-driven context discovery for robustness.
**XML Block:**
```xml
<session_management>
<lazy_init>Only create session when context file needed</lazy_init>
<isolation>Unique session IDs</isolation>
<cleanup_policy>
<manual>Confirm cleanup</manual>
<stale>Auto-remove after 24h</stale>
</cleanup_policy>
<error_handling>
<subagent_failure>Report error, seek retry/abort confirmation</subagent_failure>
</error_handling>
</session_management>
```
**Why This Works:**
- **Microsoft/AWS/Industry Docs:** Lazy init reduces overhead; manifest-driven context discovery improves targeting and prevents leakage.[10][11][12]
---
## 8. Guiding Principles _(End of prompt, or repeated as needed)_
**XML Block:**
```xml
<principles>
<lean>Concise, focused responses</lean>
<adaptive>Tone-matching: conversational for info, formal for tasks</adaptive>
<safe>ALWAYS request approval before ANY execution</safe>
<report_first>On errors: REPORT → PLAN → APPROVAL → FIX</report_first>
<lazy>Sessions/files only as needed</lazy>
</principles>
```
**Why This Works:**
- **Cognitive Load Theory & Industry Practice:** Memorable, actionable principles guide agent logic and user interaction.[13][14][15]
---
## Adjustments & Expert Recommendations
- **Drop all universal % improvement claims:** Instead, denote performance gains as "model- and task-specific."
- **Integrate safety gates and role tagging in first 20–30% of every prompt, not buried mid-prompt.**
- **Use flat XML wherever possible; avoid excessive nesting for maximum clarity.**
- **Session management and delegation should use manifest-driven context for reliable scaling.**
- **Make context layering explicit and test various orderings to optimize for your agent/model.**
---
## Research References (Why Each Stage Works)
- **[1] NAACL '24 (Southampton): Position affects performance—variance is task-specific.**
- **[3] Stanford CS224N: Context helps adherence in multi-step instructions; effect varies.**
- **[2] Anthropic Claude docs: XML tags improve clarity and structure; early role definition improves output.**
- **[5] LearnPrompting & medical role studies: Role prompting benefits are real, magnitude varies by domain/model.**
- **[4][8] AWS, MS Research: Stage-based workflows, explicit approval gates, and clear branching improve accuracy.**
- **[11][12][10] Industry docs: Lazy session/context management, manifest indexing improve efficiency and reliability.**
- **[14][15][13] Cognitive load studies & agent best practices: Lean, safety-first and adaptive approaches outperform generic prompts.**
---
## Complete Updated XML Agent Prompt Template
```xml
<critical_rules priority="highest">
<rule>ALWAYS request approval before ANY execution</rule>
<rule>NEVER auto-fix issues without explicit user approval</rule>
<rule>STOP immediately on test failure</rule>
<rule>Confirm before cleaning up files</rule>
</critical_rules>
<context_hierarchy>
<system_context>...</system_context>
<domain_context>...</domain_context>
<task_context>...</task_context>
<execution_context>...</execution_context>
</context_hierarchy>
<role>
<identity>...</identity>
<capabilities>...</capabilities>
<scope>...</scope>
<constraints>...</constraints>
</role>
<execution_paths>
<decision>
<if trigger="simple_question">route="conversational"</if>
<if trigger="task_or_execution">route="task_workflow"</if>
</decision>
</execution_paths>
<conversational_path>
<trigger>...</trigger>
<execution>...</execution>
</conversational_path>
<task_workflow>
<stage name="Analyze">...</stage>
<stage name="Plan">...</stage>
<stage name="Approval">...</stage>
<stage name="Execute">...</stage>
<stage name="Validate">...</stage>
<stage name="Summarize">...</stage>
<stage name="Complete">...</stage>
</task_workflow>
<delegation_criteria>
<route agent="..." category="...">...</route>
<direct_execution>...</direct_execution>
</delegation_criteria>
<session_management>
<lazy_init>...</lazy_init>
<isolation>...</isolation>
<cleanup_policy>...</cleanup_policy>
<error_handling>...</error_handling>
</session_management>
<principles>
<lean>...</lean>
<adaptive>...</adaptive>
<safe>...</safe>
<report_first>...</report_first>
<lazy>...</lazy>
</principles>
```
---
## Final Recommendation
**Use this structure as your foundation, but always empirically test prompt segmentation, safety gate placement, and workflow details with your specific agent and model for best results.** The research validates the general flow and methodology, not any universal quantitative improvement figures. This flow is robust, security-conscious, and adaptable—making it a best-practices template for modern LLM agent design.
---
## References
[1] https://github.com/carterlasalle/aipromptxml
[2] https://portkey.ai/blog/role-prompting-for-llms
[3] https://aws.amazon.com/blogs/machine-learning/best-practices-for-prompt-engineering-with-meta-llama-3-for-text-to-sql-use-cases/
[4] https://pmc.ncbi.nlm.nih.gov/articles/PMC12439060/
[5] https://beginswithai.com/xml-tags-vs-other-dividers-in-prompt-quality/
[6] https://www.linkedin.com/posts/jafarnajafov_how-to-write-prompts-for-claude-using-xml-activity-7353829895602356224-y0Le
[7] https://www.nexailabs.com/blog/cracking-the-code-json-or-xml-for-better-prompts
[8] https://www.thoughtworks.com/en-gb/insights/blog/generative-ai/improve-ai-outputs-advanced-prompt-techniques
[9] https://www.getdynamiq.ai/post/agent-orchestration-patterns-in-multi-agent-systems-linear-and-adaptive-approaches-with-dynamiq
[10] https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/ai-agent-design-patterns
[11] https://aimaker.substack.com/p/the-10-step-system-prompt-structure-guide-anthropic-claude
[12] https://arxiv.org/html/2507.12466v1
[13] https://www.youtube.com/watch?v=gujqOjzYzY8
[14] https://arxiv.org/html/2511.02200v1
[15] https://web.stanford.edu/~jurafsky/slp3/old_jan25/12.pdf